About this Course

48,636 recent views
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

Approx. 9 hours to complete
English

Skills you will gain

FinanceTradingInvestmentMachine Learning applied to Finance
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

Approx. 9 hours to complete
English

Offered by

Placeholder

Google Cloud

Placeholder

New York Institute of Finance

Syllabus - What you will learn from this course

Content RatingThumbs Up89%(1,952 ratings)Info
Week
1

Week 1

4 hours to complete

Introduction to Trading with Machine Learning on Google Cloud

4 hours to complete
26 videos (Total 131 min), 3 readings, 5 quizzes
26 videos
Course Overview Introduction to Trading with Machine Learning on Google Cloud5m
What is AI and ML ? What is the difference between AI and ML?58s
Applications of ML in the Real World1m
What is ML?3m
Game: The importance of good data4m
Brief History of ML in Quantitative Finance11m
Why Google?1m
Why Google Cloud Platform?2m
What are AI Platform Notebooks1m
Using Notebooks1m
Benefits of AI Platform Notebooks2m
What do we want to model? Let's start simple5m
Demo: Building a model with BigQuery ML25m
How to use Qwiklabs for your Labs3m
Lab Intro: Building a Regression Model37s
Lab Walkthrough: Building a Regression Model9m
Trading vs Investing6m
The Quant Universe2m
Quant Strategies7m
Quant Trading Advantages and Disadvantages4m
Exchange and Statistical Arbitrage8m
Index Arbitrage2m
Statistical Arbitrage Opportunities and Challenges5m
Introduction to Backtesting5m
Backtesting Design6m
3 readings
Supervised Learning and Regression10m
Welcome to Introduction to Trading, Machine Learning and GCP10m
Case Study: Capital Markets in the Cloud10m
4 practice exercises
Python Skills Assessment Quiz
AI and Machine Learning5m
Google Cloud
Trading Concepts Review15m
Week
2

Week 2

3 hours to complete

Supervised Learning with BigQuery ML

3 hours to complete
6 videos (Total 29 min), 1 reading, 3 quizzes
6 videos
What is forecasting? - part 24m
Choosing the right model and BQML - part 13m
Choosing the right model and BQML - part 22m
Lab Intro: Forecasting Stock Prices using Regression in BQML36s
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML12m
1 reading
Staying current with BigQuery ML model types10m
1 practice exercise
Forecasting
Week
3

Week 3

2 hours to complete

Time Series and ARIMA Modeling

2 hours to complete
11 videos (Total 52 min)
11 videos
AR - Auto Regressive7m
MA - Moving Average2m
The Complete ARIMA Model4m
ARIMA compared to linear regression7m
How can you get a variety of models from just a single series?1m
How to choose ARIMA parameters for your trading model4m
Time Series Terminology: Auto Correlation4m
Sensitivity of Trading Strategy4m
Lab Intro: Forecasting Stock Prices Using ARIMA32s
Lab Walkthrough: Forecasting Stock Prices using ARIMA7m
1 practice exercise
Time Series
Week
4

Week 4

1 hour to complete

Introduction to Neural Networks and Deep Learning

1 hour to complete
5 videos (Total 29 min), 1 reading, 2 quizzes
5 videos
Short history of ML: Modern Neural Networks8m
Overfitting and Underfitting6m
Validation and Training Data Splits4m
Course Recap + Preview of next course 1m
1 reading
Example BigQuery ML DNN code10m
2 practice exercises
Model generalization
Recap Quiz8m

Reviews

TOP REVIEWS FROM INTRODUCTION TO TRADING, MACHINE LEARNING & GCP

View all reviews

About the Machine Learning for Trading Specialization

Machine Learning for Trading

Frequently Asked Questions

More questions? Visit the Learner Help Center.